1. Field of the Invention
The present invention relates to image processing that applies denoising to image data having noises.
2. Description of Related Art
A widespread use of digital cameras in recent years makes it necessary to restore a digital image often taken under unfavorable conditions. In particular, an image taken at low-light intensity like in night time has noises because an amount of light inputted into the camera is small. For example, in the case of a nightwatch camera, influences of noises make it difficult to discriminate the face of an individual or a vehicle license plate number to be subjected to surveillance. In order to overcome this inconvenience, a scheme to remove a noise from an image having noises is necessary. Such a scheme is achieved by a technique generally referred to as noise reduction.
The purpose of noise reduction is to remove noises. It is, however, also desirable to preserve a detailed portion of an original image (an image having no noises) as intact as possible. For example, it is undesirable to remove a noise from an image of a license plate by noise reduction to the extent that characters on the plate are smeared and become illegible. Noise reduction therefore aims at removing noises alone as much as possible in order to preserve an edge portion of an original image as intact as possible by using a deteriorated image (an image having noises).
The simplest method of noise reduction is smooth filtering (low-pass filtering). This method cuts information of a high-frequency band where considerable noise components are contained and is therefore capable of removing noises. However, because a detailed portion (edge) of an image is also contained in the high-frequency band, sharpness is lost because of smoothing.
An example of a noise reduction scheme in consideration of edge preservation is bilateral filtering (C Tomasi and R Manduchi, “Bilateral filtering for gray and color images”, Computer Vision, 1998). Bilateral filtering is a type of weighted smooth filtering but it achieves noise reduction that enables edge preservation by devising a way to find a weighting factor. The weighting factor is determined by the following two indices.
One index is a distance difference between a reference pixel and a neighboring pixel. Given that a relation with the reference pixel becomes lower as a distance from the reference pixel becomes longer, the weighting factor is made smaller. Conversely, given that a relation with the reference pixel becomes higher as a distance from the reference pixel becomes shorter, the weighting factor is made larger.
The other index is a luminance difference between a reference pixel and a neighboring pixel. Given that a relation with the reference pixel becomes lower as a luminance difference becomes larger, the weighting factor is made smaller. Also, given that a relation with the reference pixel becomes higher as a luminance difference becomes smaller, the weighting factor is made larger.
As has been described, because the weighting factor is determined particularly by a luminance difference by bilateral filtering, edge preservation is satisfactory. Bilateral filtering has an advantage that it achieves satisfactory edge preservation by applying smooth filtering to a region where a luminance is uniform on one hand and by making the weighting factor smaller for a pixel having a large luminance difference on the other hand.
Besides the above scheme, many schemes have been proposed as a noise reduction scheme in consideration of edge preservation, for example, in JP-T-2007-536662 (hereinafter, referred to the cited reference 1) and JP-A-2006-246080 (hereinafter, referred to as the cited reference 2).
A technique described in the cited reference 1 is to calculate a weighting factor by comparing luminance differences on a block-by-block basis by bilateral filtering described above. This technique makes it possible to distinguish a texture having a spatial correlation from noises. The preservation quality of the texture can be therefore enhanced in comparison with bilateral filtering. It thus becomes possible to achieve excellent denoising performance.
According to a technique described in the cited document 2, noise reduction with excellent edge preservation is achieved by using both bilateral filtering and unsharp masking.